Center for Mathematical Sciences Merck, Oss, The Netherlands.
Anal Chim Acta. 2012 May 6;725:14-21. doi: 10.1016/j.aca.2012.03.008. Epub 2012 Mar 15.
Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised classification algorithm which has been widely used in many areas with its simplicity and its ability to deal with hidden clusters of different sizes and shapes and with noise. However, the computational issue of the distance table and the non-stability in detecting the boundaries of adjacent clusters limit the application of the original algorithm to large datasets such as images. In this paper, the DBSCAN algorithm was revised and improved for image clustering and segmentation. The proposed clustering algorithm presents two major advantages over the original one. Firstly, the revised DBSCAN algorithm made it applicable for large 3D image dataset (often with millions of pixels) by using the coordinate system of the image data. Secondly, the revised algorithm solved the non-stability issue of boundary detection in the original DBSCAN. For broader applications, the image dataset can be ordinary 3D images or in general, it can also be a classification result of other type of image data e.g. a multivariate image.
基于密度的噪声应用空间聚类(DBSCAN)是一种无监督分类算法，由于其简单性以及能够处理不同大小和形状的隐藏簇和噪声，因此已被广泛应用于许多领域。然而，距离表的计算问题和检测相邻簇边界的不稳定性限制了原始算法在大型数据集（如图像）中的应用。本文针对图像聚类和分割对 DBSCAN 算法进行了修订和改进。与原始算法相比，所提出的聚类算法具有两个主要优势。首先，通过使用图像数据的坐标系，修改后的 DBSCAN 算法使其适用于大型 3D 图像数据集（通常具有数百万个像素）。其次，修改后的算法解决了原始 DBSCAN 中边界检测的不稳定性问题。为了更广泛的应用，图像数据集可以是普通的 3D 图像，或者通常也可以是其他类型的图像数据（例如多变量图像）的分类结果。
